# Bill's Learning Track — Quant / Algorithmic

**Background:** Data scientist, ML experience, cybersecurity/scripting skills
**Role:** Build and validate — data pipelines, backtests, models, automation, alerts

---

## Phase 1 — Market Data Infrastructure

**Goal:** Get clean, reliable data into your toolchain

- [ ] TradingView Pine Script basics — indicators, alerts, screeners
- [ ] Python market data: `yfinance`, `pandas-ta`, `ccxt` (crypto if relevant)
- [ ] Free vs paid data sources — what's actually needed at this stage
- [ ] Build a basic OHLCV data fetcher for a watchlist of tickers
- [ ] Understand data quality issues: survivorship bias, split adjustments, gaps
- [ ] Set up a local data store (SQLite or Parquet) for backtesting

**Deliverable:** Script that pulls daily OHLCV for a watchlist and stores it locally

---

## Phase 2 — Indicator Implementation & Backtesting

**Goal:** Implement and test common signals before trusting them

- [ ] Implement SMA, EMA, RSI, MACD, ATR from scratch (don't just use a library — understand them)
- [ ] Build a simple backtesting loop (vectorized, not event-driven to start)
- [ ] Understand backtest traps: look-ahead bias, overfitting, survivorship bias
- [ ] Walk-forward validation basics
- [ ] Metrics: win rate, expectancy, Sharpe ratio, max drawdown, Calmar ratio
- [ ] Pine Script: build a simple strategy and view equity curve in TradingView

**Deliverable:** One complete backtest with proper train/test split and metrics reported

---

## Phase 3 — ML Signal Generation

**Goal:** Apply ML where it actually adds value (not everywhere)

- [ ] Feature engineering for price data (returns, rolling stats, lagged features)
- [ ] Classification: predict direction (up/down/flat next N bars)
- [ ] Avoid the common traps: data leakage, overfitting short samples
- [ ] Evaluate with financial metrics, not just accuracy
- [ ] Regime detection: is the market trending, ranging, or volatile?
- [ ] Understand when ML helps vs when simple rules beat it

**Deliverable:** A classifier that outputs trade signals with a documented edge (positive expectancy on out-of-sample data)

---

## Phase 4 — Automation & Alerting

**Goal:** React to markets without staring at screens

- [ ] TradingView alerts → webhook → Python handler
- [ ] Build a market watch script (runs on schedule, flags setups from Colette's criteria)
- [ ] Notification system (email, SMS, or Telegram bot)
- [ ] Paper trading automation: auto-log triggered trades to journal
- [ ] Understand execution risk: slippage, partial fills, latency (not HFT, but real)

**Deliverable:** Script that monitors a watchlist and sends an alert when Colette's setup criteria are met

---

## Phase 5 — Prop Challenge Execution

**Goal:** Apply the system under challenge conditions

- [ ] Simulate challenge rules in paper trading: daily loss limit, max drawdown, profit target
- [ ] Risk sizing formula locked in (never manual)
- [ ] Dashboard: daily P&L, drawdown remaining, progress toward profit target
- [ ] Post-trade review script: auto-generate stats from journal
- [ ] Two clean simulated challenge passes before paying

**Deliverable:** Automated prop challenge tracker with real-time status

---

## Tools & Stack

| Purpose | Tool |
|---------|------|
| Charting | TradingView |
| Data | yfinance, pandas, pandas-ta |
| Backtesting | Custom vectorized or `backtesting.py` |
| ML | scikit-learn, lightgbm |
| Automation | Python scripts + cron or systemd |
| Alerts | TradingView webhooks + custom handler |
| Storage | SQLite or Parquet files |